摘要
针对5种主次吸收峰严重交叠的红外混合气体定量分析问题,提出一种基于高阶累积量的特征提取方法,该方法将重叠的吸收谱线映射到彼此相互分开的四阶累积量谱空间;利用提取的特征向量,提出一种基于正则化统计学习理论的支持向量机的多维数据建模方法,在小样本下有效地提高了模型的精度和迭代的收敛速度。实验结果表明,该方法使系统的引用误差小于4%,因而能满足外场使用的要求。
According to feature extraction of high order cumulant, a method is proposed to solve the quantitatively analyzing of five types of gas, where the primary and secondary absorbed peaks are seriously overlapped. The absorbed spectrogram is mapped to four-order cumulant space and detached from each other. A technique is presented based on support vector machine of regularization theory to construct multi-dimension model, and accuracy and iterated rate are improved in small scale samples. The result hints the referenced error is no more than 4%, and satisfied with outfield demands.
出处
《现代科学仪器》
2006年第1期53-57,共5页
Modern Scientific Instruments
基金
国家自然科学基金(60276037)资助。
关键词
高阶累计量
支持向量机
气体分析
特征提取
傅立叶红外光谱分析
High order cumulant
support vector machine
gas analyzing
feature extraction
fourier infrared
spectrum analyzing
